{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GEN_1_mlp_pytorch.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMCncjQ7IZU0gk5loQaeb15",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cxbxmxcx/GenReality/blob/master/GEN_1_mlp_pytorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FslekLiVp_Il"
      },
      "source": [
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.autograd import Variable\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eY30bijNqNBL"
      },
      "source": [
        "class XorNet(nn.Module):\n",
        "  def __init__(self):\n",
        "    super().__init__()\n",
        "    self.fc1 = nn.Linear(2,10)\n",
        "    self.fc2 = nn.Linear(10,1)\n",
        "  def forward(self, x):\n",
        "    x = F.relu(self.fc1(x))\n",
        "    x = self.fc2(x)\n",
        "    return x"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NpNRGhi8qShC"
      },
      "source": [
        "model = XorNet()\n",
        "loss_fn = nn.MSELoss()\n",
        "optimizer = optim.Adam(model.parameters(), lr=1e-3)\n",
        "\n",
        "epochs = 500"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s8Gzcwhhr670"
      },
      "source": [
        "X = np.array([[0.,0.],[1.,1.],[0.,1.],[1.,0.]])\n",
        "Y = np.array([0.,0.,1.,1.])\n",
        "\n",
        "y_train_t = torch.from_numpy(Y).clone().reshape(-1, 1)\n",
        "x_train_t = torch.from_numpy(X).clone()\n",
        "\n",
        "history = []"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZpNxqDTNsyN3",
        "outputId": "7f2d8458-8233-4d63-fd3c-48249f585687",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for i in range(epochs):        \n",
        "  for batch_ind in range(4):\n",
        "    # wrap the data in variables\n",
        "    x_batch = Variable(torch.Tensor(x_train_t.float()))\n",
        "    y_batch = Variable(torch.Tensor(y_train_t.float()))                   \n",
        "    # forward pass\n",
        "    y_pred = model(x_batch)        \n",
        "    # compute and print loss\n",
        "    loss = loss_fn(y_pred, y_batch)\n",
        "    print(i, loss.data)\n",
        "    # reset gradients\n",
        "    optimizer.zero_grad()        \n",
        "    # backwards pass\n",
        "    loss.backward()        \n",
        "    # step the optimizer - update the weights\n",
        "    optimizer.step()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          "name": "stdout"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nXqg7ktFuUqv",
        "outputId": "f8e98cf5-2b40-445e-f5ca-c6ae0b50d1d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "v = Variable(torch.FloatTensor([1,0]))\n",
        "model(v)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1.0000], grad_fn=<AddBackward0>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yq8KAnARu_kc",
        "outputId": "efa7ce89-da5f-4f6c-bd76-56cffe94a870",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "v = Variable(torch.FloatTensor([1,1]))\n",
        "model(v)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1.5944e-06], grad_fn=<AddBackward0>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    }
  ]
}